Literature DB >> 24127130

Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon.

Dengsheng Lu1, Guiying Li, Emilio Moran, Scott Hetrick.   

Abstract

This paper provides a comparative analysis of land use and land cover (LULC) changes among three study areas with different biophysical environments in the Brazilian Amazon at multiple scales, from per-pixel, polygon, census sector, to study area. Landsat images acquired in the years of 1990/1991, 1999/2000, and 2008/2010 were used to examine LULC change trajectories with the post-classification comparison approach. A classification system composed of six classes - forest, savanna, other-vegetation (secondary succession and plantations), agro-pasture, impervious surface, and water, was designed for this study. A hierarchical-based classification method was used to classify Landsat images into thematic maps. This research shows different spatiotemporal change patterns, composition and rates among the three study areas and indicates the importance of analyzing LULC change at multiple scales. The LULC change analysis over time for entire study areas provides an overall picture of change trends, but detailed change trajectories and their spatial distributions can be better examined at a per-pixel scale. The LULC change at the polygon scale provides the information of the changes in patch sizes over time, while the LULC change at census sector scale gives new insights on how human-induced activities (e.g., urban expansion, roads, and land use history) affect LULC change patterns and rates. This research indicates the necessity to implement change detection at multiple scales for better understanding the mechanisms of LULC change patterns and rates.

Entities:  

Keywords:  Brazilian Amazon; Landsat image; land use and land cover change; spatiotemporal pattern

Year:  2013        PMID: 24127130      PMCID: PMC3795395          DOI: 10.1080/01431161.2013.802825

Source DB:  PubMed          Journal:  Int J Remote Sens        ISSN: 0143-1161            Impact factor:   3.151


  3 in total

1.  Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

Authors:  Guiying Li; Dengsheng Lu; Emilio Moran; Scott Hetrick
Journal:  Int J Remote Sens       Date:  2011       Impact factor: 3.151

2.  Land use/cover classification in the Brazilian Amazon using satellite images.

Authors:  Dengsheng Lu; Mateus Batistella; Guiying Li; Emilio Moran; Scott Hetrick; Corina da Costa Freitas; Luciano Vieira Dutra; Sidnei João Siqueira Sant'anna
Journal:  Pesqui Agropecu Bras       Date:  2012-09       Impact factor: 1.088

3.  Application of Time Series Landsat Images to Examining Land-use/Land-cover Dynamic Change.

Authors:  Dengsheng Lu; Scott Hetrick; Emilio Moran; Guiying Li
Journal:  Photogramm Eng Remote Sensing       Date:  2012-07       Impact factor: 1.083

  3 in total
  3 in total

1.  Assessment of land use and land cover change using spatiotemporal analysis of landscape: case study in south of Tehran.

Authors:  Abutaleb Sabr; Mazaher Moeinaddini; Hossein Azarnivand; Benjamin Guinot
Journal:  Environ Monit Assess       Date:  2016-11-25       Impact factor: 2.513

2.  Mapping and quantification of ferruginous outcrop savannas in the Brazilian Amazon: A challenge for biodiversity conservation.

Authors:  Pedro Walfir M Souza-Filho; Tereza C Giannini; Rodolfo Jaffé; Ana M Giulietti; Diogo C Santos; Wilson R Nascimento; José Tasso F Guimarães; Marlene F Costa; Vera L Imperatriz-Fonseca; José O Siqueira
Journal:  PLoS One       Date:  2019-01-17       Impact factor: 3.240

3.  Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation.

Authors:  Mirza Waleed; Muhammad Mubeen; Ashfaq Ahmad; Muhammad Habib-Ur-Rahman; Asad Amin; Hafiz Umar Farid; Sajjad Hussain; Mazhar Ali; Saeed Ahmad Qaisrani; Wajid Nasim; Hafiz Muhammad Rashad Javeed; Nasir Masood; Tariq Aziz; Fatma Mansour; Ayman El Sabagh
Journal:  Sci Rep       Date:  2022-08-01       Impact factor: 4.996

  3 in total

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